In this paper, the processing of sonar signals has been carried out using a Minimal Resource Allocation Network (MRAN) in identification of commonly encountered features in indoor environments. The stability-plasticity behaviors of the network have been investigated. From previous observations, the experimental results show that MRAN possesses lower network complexity but experiences higher plasticity, and is unstable. A novel approach is proposed to solve these problems in MRAN and has also been experimentally proven that the network generalizes faster at a lower number of neurons (nodes) compared to the original MRAN. This new approach has been applied as a preprocessing tool to equip the network with certain information about the data to be used in training the network later. With this initial "guidance", the network predicts extremely well in both sequential and random learning.